Guest Editors
Prof. Dr. Fábio de Oliveira Neves
Email: fabio.neves@unifal-mg.edu.br
Affiliation: Exact Science Institute. Federal University of
Alfenas, Alfenas, Minas Gerais State, 37130-001, Brazil
Homepage:
Research Interests: data with uncertainly, sustainability, energy transition, industrial energy efficiency, energy planning
Prof. Dr. Leopoldo André Dutra Lusquino Filho
Email: leopoldo.lusquino@unesp.br
Affiliation: Institute of Science and Technology, São Paulo State University (UNESP), Sorocaba, São Paulo State, Brazil
Homepage:
Research Interests: machine learning, consciousness, climate change, weightless artificial neural network
Prof. Dr. Henrique Ewbank
Email: henrique.ewbank@unt.edu
Affiliation: Department of Data Analytics and Statistics, University of North Texas, Denton, 76203-5017, United States
Homepage:
Research Interests: data science, decision making, operations management
Prof. Dr. Rafael de Oliveira Tiezzi
Email: rafaeltiezzi@ufscar.br
Affiliation: Center for Natural Sciences. Federal University of São Carlos. Buri, São Paulo State, Brazil
Homepage:
Research Interests: water resources, climate change, environmental management and sustainability engineering, energy
Summary
The global energy transition is increasingly shaped by data-driven technologies capable of enhancing efficiency, resilience, and sustainability across industrial systems. As industries face the dual challenge of decarbonization and digital transformation, integrating intelligent computational methods has become crucial to optimize resource use, reduce emissions, and ensure reliable energy management. Data-driven frameworks, combining Artificial Intelligence, Machine Learning, and advanced optimization techniques, are enabling real-time monitoring and decision-making for complex hybrid energy systems. These advances also foster new paradigms such as Digital Twins, multi-objective optimization, and life cycle–based sustainability assessment, connecting technical innovation with environmental responsibility.
This Special Issue aims to provide a multidisciplinary platform for researchers and practitioners working on intelligent energy systems to share innovative methodologies and practical applications that support industrial efficiency and renewable integration. It encourages contributions addressing theoretical, computational, and empirical approaches that bridge data analytics, system modeling, and sustainability assessment.
Topics of interest include, but are not limited to:
· Energy efficiency and multi-objective optimization in complex industrial systems;
· Data-driven modeling and Digital Twins for simulation and prediction;
· Integration of renewable energy sources (solar, wind, biogas, hydrogen);
· AI and Machine Learning in energy analysis and management;
· Fuzzy, Neuro-Fuzzy, and stochastic models for uncertainty control;
· Data science and IIoT for real-time monitoring;
· Life Cycle Assessment and sustainability indicators for decarbonization.
Keywords
data-driven energy systems, industrial efficiency, renewable integration, artificial intelligence, multi-objective optimization, fuzzy and neuro-fuzzy models, digital twins, life cycle assessment (LCA), energy transition, smart energy management
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